{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Source](https://blog.keras.io/building-autoencoders-in-keras.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "from keras.datasets import mnist\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this is the size of our encoded representations\n",
    "encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats\n",
    "\n",
    "# this is our input placeholder\n",
    "input_img = Input(shape=(784,))\n",
    "# \"encoded\" is the encoded representation of the input\n",
    "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
    "# \"decoded\" is the lossy reconstruction of the input\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this model maps an input to its reconstruction\n",
    "autoencoder = Model(input_img, decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this model maps an input to its encoded representation\n",
    "encoder = Model(input_img, encoded)\n",
    "\n",
    "# create a placeholder for an encoded (32-dimensional) input\n",
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "# retrieve the last layer of the autoencoder model\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "# create the decoder model\n",
    "decoder = Model(encoded_input, decoder_layer(encoded_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n"
     ]
    }
   ],
   "source": [
    "(x_train, _), (x_test, _) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "print x_train.shape\n",
    "print x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.3701 - val_loss: 0.2730\n",
      "Epoch 2/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2664 - val_loss: 0.2565\n",
      "Epoch 3/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2460 - val_loss: 0.2329\n",
      "Epoch 4/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2240 - val_loss: 0.2130\n",
      "Epoch 5/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.2073 - val_loss: 0.1992\n",
      "Epoch 6/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1959 - val_loss: 0.1899\n",
      "Epoch 7/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1878 - val_loss: 0.1829\n",
      "Epoch 8/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1814 - val_loss: 0.1771\n",
      "Epoch 9/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1760 - val_loss: 0.1721\n",
      "Epoch 10/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1712 - val_loss: 0.1677\n",
      "Epoch 11/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1669 - val_loss: 0.1635\n",
      "Epoch 12/50\n",
      "60000/60000 [==============================] - 2s - loss: 0.1630 - val_loss: 0.1598\n",
      "Epoch 13/50\n",
      "60000/60000 [==============================] - 2s - loss: 0.1594 - val_loss: 0.1563\n",
      "Epoch 14/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1559 - val_loss: 0.1528\n",
      "Epoch 15/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1526 - val_loss: 0.1496\n",
      "Epoch 16/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1494 - val_loss: 0.1464\n",
      "Epoch 17/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1464 - val_loss: 0.1436\n",
      "Epoch 18/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1436 - val_loss: 0.1408\n",
      "Epoch 19/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1410 - val_loss: 0.1382\n",
      "Epoch 20/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1386 - val_loss: 0.1359\n",
      "Epoch 21/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1363 - val_loss: 0.1337\n",
      "Epoch 22/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1342 - val_loss: 0.1316\n",
      "Epoch 23/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1322 - val_loss: 0.1297\n",
      "Epoch 24/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1303 - val_loss: 0.1278\n",
      "Epoch 25/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1286 - val_loss: 0.1260\n",
      "Epoch 26/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1268 - val_loss: 0.1244\n",
      "Epoch 27/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1252 - val_loss: 0.1227\n",
      "Epoch 28/50\n",
      "60000/60000 [==============================] - 2s - loss: 0.1237 - val_loss: 0.1213\n",
      "Epoch 29/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1222 - val_loss: 0.1198\n",
      "Epoch 30/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1209 - val_loss: 0.1185\n",
      "Epoch 31/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1196 - val_loss: 0.1172\n",
      "Epoch 32/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1183 - val_loss: 0.1160\n",
      "Epoch 33/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1172 - val_loss: 0.1149\n",
      "Epoch 34/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1161 - val_loss: 0.1139\n",
      "Epoch 35/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1151 - val_loss: 0.1129\n",
      "Epoch 36/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1141 - val_loss: 0.1120\n",
      "Epoch 37/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1132 - val_loss: 0.1111\n",
      "Epoch 38/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1123 - val_loss: 0.1103\n",
      "Epoch 39/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1115 - val_loss: 0.1095\n",
      "Epoch 40/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1108 - val_loss: 0.1088\n",
      "Epoch 41/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1101 - val_loss: 0.1081\n",
      "Epoch 42/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1095 - val_loss: 0.1075\n",
      "Epoch 43/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1089 - val_loss: 0.1069\n",
      "Epoch 44/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1083 - val_loss: 0.1063\n",
      "Epoch 45/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1078 - val_loss: 0.1059\n",
      "Epoch 46/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1073 - val_loss: 0.1054\n",
      "Epoch 47/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1068 - val_loss: 0.1049\n",
      "Epoch 48/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1064 - val_loss: 0.1045\n",
      "Epoch 49/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1060 - val_loss: 0.1041\n",
      "Epoch 50/50\n",
      "60000/60000 [==============================] - 3s - loss: 0.1056 - val_loss: 0.1038\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f26d90e6d90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=50,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# encode and decode some digits\n",
    "# note that we take them from the *test* set\n",
    "encoded_imgs = encoder.predict(x_test)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H40G5XIbVasVgMEC325W/r5+jlwfVf88wDEmiTSaTSCaTSCQS4qfpcrngdrtvKG7UGoMN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\nh4eHh8cbwf8DlFr2xcrUKJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f26d8ff0c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use Matplotlib (don't ask)\n",
    "\n",
    "n = 10  # how many digits we will display\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Adding a sparsity constraint on the encoded representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
